Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives

Genome-wide analysis of WRKY transcription factor genes in Toona sinensis: An insight into evolutionary characteristics and terpene synthesis

WRKY transcription factors (TFs), one of the largest TF families, serve critical roles in the regulation of secondary metabolite production. However, little is known about the expression pattern of WRKY genes during the germination and maturation processes of Toona sinensis buds. In the present study, the new assembly of the T. sinensis genome was used for the identification of 78 TsWRKY genes, including gene structures, phylogenetic features, chromosomal locations, conserved protein domains, cis-regulatory elements, synteny, and expression profiles. Gene duplication analysis revealed that gene tandem and segmental duplication events drove the expansion of the TsWRKYs family, with the latter playing a key role in the creation of new TsWRKY genes. The synteny and evolutionary constraint analyses of the WRKY proteins among T. sinensis and several distinct species provided more detailed evidence of gene evolution for TsWRKYs. Besides, the expression patterns and co-expression network analysis show TsWRKYs may multi-genes co-participate in regulating terpenoid biosynthesis. The findings revealed that TsWRKYs potentially play a regulatory role in secondary metabolite synthesis, forming the basis for further functional characterization of WRKY genes with the intention of improving T. sinensis

Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization

Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives

The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study

Evidence-based intervention and policy strategies to address the recent surge of race-motivated hate crimes and other forms of racism against Asian Americans are essential; however, such efforts have been impeded by a lack of empirical knowledge, e.g., about racism, specifically aimed at the Asian American population. Our qualitative descriptive study sought to fill this gap by using a data-mining approach to examine the contents of tweets having the hashtag #StopAsianHate. We collected tweets during a two-week time frame starting on 20 May 2021, when President Joe Biden signed the COVID-19 Hate Crimes Act. Screening of the 31,665 tweets collected revealed that a total of 904 tweets were eligible for thematic analysis. Our analysis revealed five themes: “Asian hate is not new”, “Address the harm of racism”, “Get involved in #StopAsianHate”, “Appreciate the Asian American and Pacific Islander (AAPI) community’s culture, history, and contributions” and “Increase the visibility of the AAPI community.” Lessons learned from our findings can serve as a foundation for evidence-based strategies to address racism against Asian Americans both locally and globally

Genome-Wide Identification and Expression Analysis of Dof Transcription Factors in Lotus (Nelumbo nucifera Gaertn.)

Lotus (Nelumbo nucifera Gaertn.) is a traditional Chinese aquatic flower with high ornamental and economic value, but water salinity seriously affects lotus cultivation and distribution. The Dof transcription factors (TFs) play a crucial function in the regulatory network of growth and defense in plants. However, no systematic investigations of the Dof TFs in lotus have been performed. In this study, comprehensive searches of the lotus genome yielded 29 potential NnDofs. We carried out a series of standardized analyses, which include physical properties, multiple sequence alignment, phylogenetic analysis, gene structure, motif composition, cis-acting element prediction, chromosome distribution, and synteny analysis. The results showed that segment duplication probably caused the NnDofs gene family expansion. The potential functions of NnDofs in lotus development and stress conditions are speculated by promoter analysis. Furthermore, a complete expression investigation of NnDofs utilizing an RNA-seq atlas and quantitative real-time polymerase chain reaction (qRT-PCR) was performed. The majority of the NnDofs exhibit tissue-specific expression patterns, and many genes have been identified as being extremely sensitive to salt stressors. Overall, this study is the first to report a genome-wide assessment of the Dof family in lotus, and the findings offer vital insights for prospective functional studies on lotus salinity stress

Mainly Dimers and Trimers of Chinese Bayberry Leaves Proanthocyanidins (BLPs) are Utilized by Gut Microbiota: In Vitro Digestion and Fermentation Coupled with Caco-2 Transportation

Chinese bayberry leaf proanthocyanidins (BLPs) are Epigallocatechin gallate (EGCG) oligomers or polymers, which have a lot of health-promoting activity. The activity is closely related to their behavior during in vitro digestion, which remains unknown and hinders further investigations. To clarify the changes of BLPs during gastrointestinal digestion, further research is required. For in vitro digestion, including gastric-intestinal digestion, colon fermentation was applied. Caco-2 monolayer transportation was also applied to investigate the behavior of different BLPs with different degrees of polymerization. The trimers and the tetramers were significantly decreased during in vitro gastric-intestinal digestion resulting in a significant increase in the content of dimers. The dimers and trimers were the main compounds utilized by gut microbiota and they were assumed not to degrade through cleavage of the inflavan bond. The monomers and dimers were able to transport through the Caco-2 monolayer at a rate of 10.45% and 6.4%, respectively

Parameters evaluation of fault-karst carbonate reservoirs with vertical beads-on-string structure based on bottom-hole pressure: Case studies in Shunbei Oilfield, Tarim Basin of Northwestern China

Tarim Basin newly discovered the fault-karst carbonate reservoirs, which are formed by the large-scale tectonic fault activities and multiple-stage karstification. Four kinds of mediums coexist in the reservoirs, including the large cave, vug, tectonic fracture and matrix. The tectonic fractures interconnect with large caves in series to form the vertical beads-on-string structure, which is the most common connection pattern in reservoirs. To provide a well test method for evaluating this type of structure, this work firstly presents a multi-fracture-region multi-cave-region series connection physical model by simplifying vertical beads-on-string structure. We consider four kinds of mediums in the proposed physical model, including large caves, small vugs, high-angle tectonic fracture and rock matrix. The fracture regions mainly contain fracture, vug and matrix mediums. The cave regions contain cave medium. The corresponding mathematical model is also developed, in which the flow in fracture regions obeys the Darcy’s law, while the flow in cave regions is assumed to obey free flow. Furthermore, the gravity is taken into account because the flow is along the vertical direction. Then the typical flow regimes are analyzed and sensitivity analysis is conducted on crucial parameters. Results indicate that (a) the typical feature of vertical beads-on-string structure on type curves is that the cave storage regimes and linear flow regimes alternately appear; (b) the type curves will exhibit the cave storage regimes with unit-slope pressure derivative for the existence of large caves, which is different from the inter-porosity flow regimes for the existence of the vugs (slope ≠ 1); (c) the gravity effect could lead to unit-slope pressure and pressure derivative curves, which can be regarded as closed boundary in a peculiar sense; (d) gravity effect is difficult to be observed from well test curves with about 2-weeks test duration in real application. Finally, two cases from Shunbei Oilfield are interpreted to illustrate the practicability and feasibility of proposed method

Removal of Hexavalent Chromium by Electrospun Silicon Dioxide Nanofibers Embedded with Copper-Based Organic Frameworks

A novel adsorbent copper-based organic skeleton/silicon dioxide (HKUST-1/SiO2) composite nanofiber, which can effectively remove Cr (VI) under synergistic action, has been prepared by embedding growth technique. This adsorbent was characterized by embedded growth of HKUST-1 on inorganic SiO2 electrospun nanofibers, which can remove Cr (VI) in water with the help of adsorption and membrane separation under synergistic action. The results revealed that HKUST-1 was successfully embedded between the pores of SiO2 electrospun nanofibers. The factors affecting the adsorption performance of the composite nanofibers were studied, and the result displayed that the concentration of Cr (VI) solution was 120 mg/L, the best range for pH was 3~7, the adsorption equilibrium was about 45 min, and the maximum adsorption amount was 62.38 mg/g. Compared with the SiO2 fiber without HKUST-1 growth, the adsorptive property of the composite fiber was significantly increased by 15 mg/g. The adsorption process was spontaneous and belonged to the heat absorption reaction, which was consistent with Langmuir adsorption and the pseudo-second-order kinetic model. In addition, HKUST-1/SiO2 NFs can be used for the recovery of chromium resources because the HKUST-1/SiO2 NFs captured Cr (VI) can be calcined and recovered in the later stage, which reduces the consumption of desorption liquid, simplifies the recovery steps, and is conducive to energy saving and emission reduction. Therefore, HKUST-1/SiO2 NFs are expected to be applied in the field of hexavalent chromium wastewater purification and resource recovery